/Project |-Database/ |-Binaries/ |-Figures/ |-hawaii |_ beakerBanter_hawaii.Rmd |_ hawaii_banter_data.rds |_ hawaii_banter_data_ici.rds |_ hawaii_banter_model_t5e3s10_t1e4s4.rds |_ hawaii_banter_model_ici_t5e3s5_t1e4s4.rds
Start by loading the required packages
library("easypackages")
## Warning: package 'easypackages' was built under R version 4.2.3
libraries("PAMpal", "banter", "rfPermute", "kableExtra", "magick", "magrittr", "here")
## Warning: package 'dplyr' was built under R version 4.2.3
## Warning: package 'banter' was built under R version 4.2.3
## Warning: package 'magick' was built under R version 4.2.3
here()
## [1] "C:/Users/shannon.rankin/Documents/GitHub/BANTER_BeakedWhales"
pps <- PAMpalSettings(db='Database/',
binaries = 'Binaries/',
sr_hz='auto',
winLen_sec=.0025,
filterfrom_khz=10,
filterto_khz=NULL)
If this is the initial processing, ensure you have set ‘freshRun = TRUE’ at top of this document to process and save data. This will take some time to run.
data <- processPgDetections(pps, mode='db', id='hawaii_bw')
saveRDS(data, 'hawaii_study.rds')
# Double check warning messages
print(getWarnings(data)$message)
If you have already run the processing code, ensure you have set ‘freshRun = FALSE’ at top of this document to read in the existing .rds file for downstream processing.
data <- setSpecies(data, 'pamguard')
reSpecies <- readRDS('species.rds')
print(reSpecies)
data <- setSpecies(data, 'reassign', value=reSpecies)
goodSpecies<- c("ZC", "MD", "BW", "BWC", "IP", "BW", "possBW")
data <- filter(data, species %in% goodSpecies)
data <- setSpecies(data, method='reassign',
value=data.frame(old=c('ZC', 'MD', 'BWC', 'IP'), new=c("Cuviers", "Blainsvilles", "CrossSeamount", "Longmans")))
data_ch1only <- filter(data, Channel == '1')
data_ch1only <- calculateICI(data_ch1only, time='peakTime')
banter_data <- export_banter(data_ch1only, dropSpecies = c('BW', 'possBW'),
dropVars = c('All_ici'), training=TRUE)
saveRDS(banter_data, file='hawaii_banter_data.rds')
banter_data_ici <- export_banter(data_ch1only, dropSpecies = c("BW", "possBW"), training=TRUE)
saveRDS(banter_data_ici, file='hawaii_banter_data_ici.rds')
#save update of Acoustic Study
saveRDS(data_ch1only, 'hawaii_study.rds')
Initialize, Run & Evaluate Detector Model (stage 1).
banter_model_ec <- initBanterModel(banter_data$events)
banter_model_ec <- addBanterDetector(banter_model_ec, banter_data$detectors, ntree=5e3, sampsize=10, importance = TRUE)
plotDetectorTrace(banter_model_ec, detector = paste0('Click_Detector_', 0:3))
plotDetectorTrace(banter_model_ec, detector = paste0('Click_Detector_', 4:6))
summary(banter_model_ec)
Run BANTER Event Model (stage 2)
banter_model_ec <- runBanterModel(banter_model_ec, ntree=1e4, sampsize=4)
summary(banter_model_ec)
Once a stable model is identified, save model with tree/sampsize info in the filename.
saveRDS(banter_model_ec, 'hawaii_banter_model_ec_t5e3s10_t1e4s4.rds')
Initialize, Run & Evaluate Detector Model (stage 1)
banter_model_ici <- initBanterModel(banter_data_ici$events)
banter_model_ici <- addBanterDetector(banter_model_ici, banter_data_ici$detectors, ntree=5e3, sampsize=5, importance = TRUE)
plotDetectorTrace(banter_model_ici, detector = paste0('Click_Detector_', 0:3))
plotDetectorTrace(banter_model_ici, detector = paste0('Click_Detector_', 4:6))
summary(banter_model_ici)
Run BANTER Event Model (stage 2)
banter_model_ici <- runBanterModel(banter_model_ici, ntree=1e4, sampsize=4)
summary(banter_model_ici)
Once a stable model is identified, save model with tree/sampsize info in the filename.
saveRDS(banter_model_ici, 'hawaii_banter_model_ici_t5e3s5_t1e4s4.rds')
There are a number of visualizations/data products that allow us to visualize our BANTER classifier; most use the rfPermute package (see BANTER Guidelines for more information)
First, identify the model you would like to examine (comment out the model you do not want to examine).
model_ec <- banter_model_ec
modelname_ec <- "banter_model_ec"
model_ici <- banter_model_ici
modelname_ici <- "banter_model_ici"
Extract the Random Forest model object from our BANTER model for analysis.
banter_model_ec_RF <- getBanterModel(model_ec)
banter_model_ici_RF <- getBanterModel(model_ici)
Class Priors (Expected Error Rate)
hawaii_ec_priors <- classPriors(banter_model_ec_RF, NULL)[,1]
hawaii_ici_priors <- classPriors(banter_model_ici_RF, NULL)[,1]
Confusion Matrix
hawaii_ec_confuseMatrix <- rfPermute::confusionMatrix(banter_model_ec_RF)
hawaii_ec_confuseMatrix <- cbind(hawaii_ec_confuseMatrix, priors = hawaii_ec_priors)
hawaii_ec_confuseMatrix <- kable(hawaii_ec_confuseMatrix, align = "c", digits = c(0,0,0,0,2,2,2))%>%
kable_classic()%>%
column_spec(5, border_right = TRUE)%>%
row_spec(0, bold = TRUE)%>%
row_spec(5,hline_after = TRUE)%>%
row_spec(5, bold = TRUE)%>%
save_kable('../manuscript/manuscript_files/hawaii_ec_confuseMatrix.png', zoom = 9)
hawaii_ici_confuseMatrix <- rfPermute::confusionMatrix(banter_model_ici_RF)
hawaii_ici_confuseMatrix <- cbind(hawaii_ici_confuseMatrix, priors = hawaii_ici_priors)
hawaii_ici_confuseMatrix <- kable(hawaii_ici_confuseMatrix, align = "c", digits = c(0,0,0,0,2,2,2))%>%
kable_classic()%>%
column_spec(5, border_right = TRUE)%>%
row_spec(0, bold = TRUE)%>%
row_spec(5,hline_after = TRUE)%>%
row_spec(5, bold = TRUE)%>%
save_kable('../manuscript/manuscript_files/hawaii_ici_confuseMatrix.png', zoom = 9)
BANTER Model Hawaii EC Confusion Matrix
BANTER Model Hawaii ICI Confusion Matrix
Proximity Plot
png(('../manuscript/manuscript_files/hawaii_ec_proximity.png'), width = 20, height = 20, units = 'cm', res = 300)
plotProximity(banter_model_ec_RF)
dev.off()
ec_hawaii_proximityPlot <- plotProximity(banter_model_ec_RF)
png(('../manuscript/manuscript_files/hawaii_ici_proximity.png'), width = 20, height = 20, units = 'cm', res = 300)
ici_hawaii_proximityPlot <- plotProximity(banter_model_ici_RF)
dev.off()
ici_hawaii_proximityPlot <- plotProximity(banter_model_ici_RF)
Importance Heatmap
png(('../manuscript/manuscript_files/hawaii_ec_importance.png'), width = 30, height = 25, units = 'cm', res = 300)
plotImportance(banter_model_ec_RF, plot.type="heatmap")
dev.off()
ec_hawaii_importance <- plotImportance(banter_model_ec_RF, plot.type="heatmap")
png(('../manuscript/manuscript_files/hawaii_ici_importance.png'), width = 30, height = 25, units = 'cm', res = 300)
plotImportance(banter_model_ici_RF, plot.type="heatmap")
dev.off()
ici_hawaii_importance <- plotImportance(banter_model_ici_RF, plot.type="heatmap")
PlotVotes
png(('../manuscript/manuscript_files/hawaii_ec_votes.png'), width = 20, height = 20, units = 'cm', res = 300)
plotVotes(banter_model_ec_RF)
dev.off()
hawaii_votes <- plotVotes(banter_model_ec_RF)
png(('../manuscript/manuscript_files/hawaii_ici_votes.png'), width = 20, height = 20, units = 'cm', res = 300)
plotVotes(banter_model_ici_RF)
dev.off()
ici_hawaii_votes <- plotVotes(banter_model_ici_RF)
Plot Predicted Probabilities
plotPredictedProbs(banter_model_ec_RF, bins=30, plot=TRUE)
plotPredictedProbs(banter_model_ici_RF, bins=30, plot=TRUE)
Create Figure for Publication
confuse <- magick::image_read(here('manuscript', 'manuscript_files', 'hawaii_ec_confuseMatrix.png'))%>%
image_border(color="#ffffff", geometry = "50x130")%>%
image_annotate("a) Confusion Matrix", size=300, color = "black")
vote <- magick::image_read(here('manuscript', 'manuscript_files', 'hawaii_ec_votes.png'))%>%
image_border(color="#ffffff", geometry = "270x130")%>%
image_annotate("d) Vote Plot", size=300, color = "black")
prox <- magick::image_read(here('manuscript', 'manuscript_files', 'hawaii_ec_proximity.png'))%>%
image_border(color="#ffffff", geometry = "270x130")%>%
image_annotate("b) Proximity Plot", size=300, color = "black")
heat <- magick::image_read(here('manuscript', 'manuscript_files', 'hawaii_ec_importance.png'))%>%
image_border(color="#ffffff", geometry = "270x130")%>%
image_scale("3300")%>%
image_annotate("d) Importance Heat Map", size=300, color = "black")
hawaii_ec_Figure <-image_append(c(prox, heat, vote))
hawaii_ec_Figure<- image_append(c(confuse, hawaii_ec_Figure), stack=TRUE)
image_write(hawaii_ec_Figure, path = here('manuscript', 'manuscript_files','hawaii_ec_Figure.png'), format ='png')
print(hawaii_ec_Figure, info=FALSE)
confuse <- magick::image_read(here('manuscript', 'manuscript_files', 'hawaii_ici_confuseMatrix.png'))%>%
image_border(color="#ffffff", geometry = "50x130")%>%
image_annotate("a) Confusion Matrix", size=300, color = "black")
vote <- magick::image_read(here('manuscript', 'manuscript_files', 'hawaii_ici_votes.png'))%>%
image_border(color="#ffffff", geometry = "270x130")%>%
image_annotate("d) Vote Plot", size=300, color = "black")
prox <- magick::image_read(here('manuscript', 'manuscript_files', 'hawaii_ici_proximity.png'))%>%
image_border(color="#ffffff", geometry = "270x130")%>%
image_annotate("b) Proximity Plot", size=300, color = "black")
heat <- magick::image_read(here('manuscript', 'manuscript_files', 'hawaii_ici_importance.png'))%>%
image_border(color="#ffffff", geometry = "270x130")%>%
image_scale("3300")%>%
image_annotate("d) Importance Heat Map", size=300, color = "black")
hawaii_ici_Figure <-image_append(c(prox, heat, vote))
hawaii_ici_Figure<- image_append(c(confuse, hawaii_ici_Figure), stack=TRUE)
image_write(hawaii_ici_Figure, path = here('manuscript', 'manuscript_files','hawaii_ici_Figure.png'), format ='png')
print(hawaii_ici_Figure, info=FALSE)